The emergence of Large Language Models (LLMs) demonstrates their potential to encapsulate the logic and patterns inherent in human behavior simulation by leveraging extensive web data pre-training. However, the boundaries of LLM capabilities in social simulation remain unclear. To further explore the social attributes of LLMs, we introduce the CiteAgent framework, designed to generate citation networks based on human-behavior simulation with LLM-based agents. CiteAgent successfully captures predominant phenomena in real-world citation networks, including power-law distribution, citational distortion, and shrinking diameter. Building on this realistic simulation, we establish two LLM-based research paradigms in social science: LLM-SE (LLM-based Survey Experiment) and LLM-LE (LLM-based Laboratory Experiment). These paradigms facilitate rigorous analyses of citation network phenomena, allowing us to validate and challenge existing theories. Additionally, we extend the research scope of traditional science of science studies through idealized social experiments, with the simulation experiment results providing valuable insights for real-world academic environments. Our work demonstrates the potential of LLMs for advancing science of science research in social science.

翻译：大型语言模型（LLMs）的出现展示了其通过大规模网络数据预训练来捕捉人类行为模拟中内在逻辑与模式的潜力。然而，LLMs在社会模拟中的能力边界尚不明确。为了进一步探索LLMs的社会属性，我们提出了CiteAgent框架，旨在通过基于LLM的智能体生成基于人类行为模拟的引文网络。CiteAgent成功捕捉了现实世界引文网络中的主要现象，包括幂律分布、引用失真和收缩直径。基于这一现实模拟，我们建立了两种基于LLM的社会科学研究范式：LLM-SE（基于LLM的调查实验）和LLM-LE（基于LLM的实验室实验）。这些范式支持对引文网络现象进行严格分析，使我们能够验证和挑战现有理论。此外，我们通过理想化的社会实验拓展了传统科学学研究的研究范围，模拟实验结果也为现实学术环境提供了有价值的见解。我们的工作证明了LLMs在推动社会科学中科学研究方面的潜力。